Collaborative filtering is a recommendation technique that predicts user preferences by analyzing interactions between users and items. It operates on the principle that users who agreed in the past (e.g., liked similar movies) will agree again in the future, and items with similar user interactions can be grouped. This method doesn’t require explicit item features (like genre or price) but relies on user behavior data such as ratings, clicks, or purchases. For example, if User A and User B both rated five action movies highly, the system might recommend action films User B liked to User A, even if those films differ in director or release year.
There are two primary approaches: user-based and item-based filtering. User-based methods identify users with similar preferences (neighbors) and recommend items those neighbors liked. For instance, if three users who love sci-fi and comedy all rated “The Martian” highly, the system might suggest it to a fourth user with similar tastes. Item-based methods focus on item similarities instead. If users who watched “Inception” also watched “Interstellar,” the system treats these movies as related and recommends one to viewers of the other. Both approaches typically use similarity metrics like Pearson correlation or cosine similarity to quantify relationships. A key challenge is handling sparse data—when most users interact with few items, the user-item matrix becomes sparse, reducing recommendation accuracy.
To address limitations like data sparsity or the “cold start” problem (new users/items with no interaction history), developers often combine collaborative filtering with other techniques. For example, hybrid systems might blend it with content-based filtering (using item features like text descriptions) to bootstrap recommendations for new items. Platforms like Netflix use such hybrids, combining viewing history (collaborative data) with metadata like genre or actor information. Additionally, implicit feedback (e.g., clicks, time spent) can supplement explicit ratings to improve coverage. While collaborative filtering scales well with large datasets, efficient implementations often require dimensionality reduction (e.g., matrix factorization) or neighborhood pruning to optimize performance in production systems.